Run Gemma 4 12B on a 16 GB Laptop – Near‑26B MoE Performance via Encoder‑Free Design

Google DeepMind announced the Gemma 4 12B model, a 12‑billion‑parameter multimodal model that achieves inference, code generation, and multimodal understanding results comparable to the 26‑billion‑parameter Gemma 4 26B MoE model. Official benchmarks show its performance is close to the larger MoE model while requiring only 16 GB of GPU or unified memory, enabling local execution on consumer laptops.

The key innovation is an Encoder‑Free architecture. Instead of the traditional "encoder + LLM" pipeline where images are processed by a visual encoder and audio by a speech encoder before feeding a language model, Gemma 4 12B embeds images through a lightweight module directly into the LLM backbone and projects audio tokens into the same representation space as text. A single Decoder‑Only Transformer then handles text, image, and audio modalities, reducing multimodal inference latency, system complexity, and memory usage.

Additional capabilities include a 256K context window, a switchable "Thinking" deep‑reasoning mode, native function calling, and agent workflow support. In standard evaluations the model’s overall score approaches that of the 26 B MoE version, yet its runtime cost is less than half.

HyperAI has released a notebook‑based "one‑click deployment" for Gemma 4 12B‑it, lowering the barrier for developers to test the model. The deployment steps are:

Visit the HyperAI homepage, navigate to the Tutorials section, select "One‑click Deploy Gemma 4 12B‑it", and click "Run this tutorial".

On the tutorial page, click the top‑right Clone button to copy the notebook repository into your own container.

Choose the NVIDIA RTX 5090 hardware profile and the vLLM image, then press "Continue job execution".

Wait for the job to be allocated; once the status changes to Running , click Open Workspace to enter the Jupyter environment.

Inside the workspace, open the README file and click Run to start the model.

After execution finishes, click the displayed API address to open the demo interface and explore the model’s multimodal capabilities.

Images in the original article illustrate the notebook interface, resource allocation screens, and demo outputs, confirming that the model can be interacted with directly after deployment.